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Transport across Membranes: Techniques for Measuring Efflux in Fungal Cells
One of the most prevalent mechanisms of antifungal drug resistance is export of the molecule from the fungal cells through the action of putative...
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Luciferase-Based High-Throughput Screen with Aspergillus fumigatus to Identify Antifungal Small Molecules
Only three classes of contemporary antifungal drugs are routinely utilized in the clinic against filamentous fungal pathogens such as Aspergillus...
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Copy Number Variation and Allele Ratio Analysis in Candida albicans Using Whole Genome Sequencing Data
Whole genome sequencing of human fungal pathogens has revolutionized the speed and accuracy in which sequence variants that cause antifungal...
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A Dual-Readout High-Throughput Screening Assay for Small Molecules Active Against Aspergillus Fumigatus
Human fungal infections caused by molds have been on the rise in recent years. These infections have high mortality rates compared to other fungal...
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Protocols for Measuring Tolerant and Heteroresistant Drug Responses of Pathogenic Yeasts
The classic definition of antimicrobial susceptibility to antifungal drugs ignores the persistence of subpopulations that survive in the presence of...
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In Silico Models for Predicting Acute Systemic Toxicity
In this chapter, we give a brief overview of the regulatory requirements for acute systemic toxicity information in the European Union, and we review...
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In Silico Models for Skin Sensitization and Irritation
The assessment of skin irritation, and in particular of skin sensitization, has undergone an evolution process over the last years, pushing forward...
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Using VEGAHUB Within a Weight-of-Evidence Strategy
Industrial needs and regulatory requirements have played a significant role in accelerating the use of nontesting methods including in silico tools...
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Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry
The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest...
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In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives
This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of...
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In Silico Tools and Software to Predict ADMET of New Drug Candidates
Implication of computational techniques and in silico tools promote not only reduction of animal experimentations but also save time and money...
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In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions
Information on genotoxicity is an essential piece of information in the framework of several regulations aimed at evaluating chemical toxicity. In...
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Deep Learning in Structure-Based Drug Design
Computational methods play an increasingly important role in drug discovery. Structure-based drug design (SBDD), in particular, includes techniques...
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Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction
The improvement in the ability of the pharmaceutical industry to predict human pharmacokinetic behavior are attributable to major technological...
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Artificial Intelligence and Quantum Computing as the Next Pharma Disruptors
Artificial intelligence (AI) consists of a synergistic assembly of enhanced optimization strategies with wide application in drug discovery and...
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Artificial Intelligence–Enabled De Novo Design of Novel Compounds that Are Synthesizable
Development of computer-aided de novo design methods to discover novel compounds in a speedy manner to treat human diseases has been of interest to...
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Has Artificial Intelligence Impacted Drug Discovery?
Artificial intelligence (AI) tools find increasing application in drug discovery supporting every stage of the Design-Make-Test-Analyse (DMTA) cycle....
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Fighting COVID-19 with Artificial Intelligence
The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a...
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Application of Artificial Intelligence and Machine Learning in Drug Discovery
Machine Learning (ML) and Deep Learning (DL) are two subclasses of Artificial Intelligence (AI), that, in this day and age of big data provides...
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Network-Driven Drug Discovery
We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and...